I Spent Two Weeks Testing Chinese AI Models — Here's What I Found
I graduated from a coding bootcamp three months ago, and I'll be honest — when I first heard people talking about Chinese AI models, I kind of brushed it off. I figured, "Why would I use some random model when ChatGPT exists?" I had no idea what I was missing.
Then a buddy from my cohort showed me his API bill. The guy had been building a documentation generator for two months and he'd spent something like $40 on OpenAI. I was shocked. I was building something similar and had already hit $25 in week one. Something had to give.
That's when he told me about these Chinese models. DeepSeek, Qwen, Kimi, GLM. I'd heard the names but never tried them. Honestly, I didn't even know they were OpenAI-compatible, which felt like a stupid thing not to know as a developer. Anyway, I blocked off two weekends and decided to actually test them properly. Here's everything I learned.
My "Wait, What?" Moment
The biggest surprise was finding out I could access all four of these model families through one endpoint. Global API had a unified interface that works with OpenAI's SDK. So I didn't have to mess with four different APIs, four different keys, four different docs. Just one base URL swap and I was off.
Here's the thing they don't tell you in bootcamp: the model landscape is way more crowded than OpenAI and Anthropic. China's been building like crazy. We're talking DeepSeek from 幻方, Qwen from Alibaba, Kimi from Moonshot AI (月之暗面), and GLM from Zhipu AI (智谱). Four big players, each with their own thing going on.
I ran them through the same set of tasks — coding challenges, English writing, Chinese writing, math problems, and speed tests. Here's the rundown.
The Numbers at a Glance
Before I get into my personal experience with each one, here's the cheat sheet I made for myself. It covers the price ranges and what each family does best.
| Feature | DeepSeek | Qwen | Kimi | GLM |
|---|---|---|---|---|
| Developer | DeepSeek (幻方) | Alibaba (阿里) | Moonshot AI (月之暗面) | Zhipu AI (智谱) |
| Price Range | $0.25-$2.50/M | $0.01-$3.20/M | $3.00-$3.50/M | $0.01-$1.92/M |
| Best Budget Model | V4 Flash @ $0.25/M | Qwen3-8B @ $0.01/M | N/A (all premium) | GLM-4-9B @ $0.01/M |
| Best Overall | V4 Flash @ $0.25/M | Qwen3-32B @ $0.28/M | K2.5 @ $3.00/M | GLM-5 @ $1.92/M |
| Code Generation | ⭐⭐⭐⭐⭐ | ⭐⭐⭐⭐ | ⭐⭐⭐⭐ | ⭐⭐⭐ |
| Chinese Language | ⭐⭐⭐⭐ | ⭐⭐⭐⭐ | ⭐⭐⭐⭐⭐ | ⭐⭐⭐⭐⭐ |
| English Language | ⭐⭐⭐⭐⭐ | ⭐⭐⭐⭐ | ⭐⭐⭐⭐ | ⭐⭐⭐⭐ |
| Reasoning | ⭐⭐⭐⭐ | ⭐⭐⭐⭐ | ⭐⭐⭐⭐⭐ | ⭐⭐⭐⭐ |
| Speed | ⭐⭐⭐⭐⭐ | ⭐⭐⭐⭐ | ⭐⭐⭐ | ⭐⭐⭐⭐ |
| Vision/Multimodal | Limited | ✅ (VL, Omni) | ❌ | ✅ (GLM-4.6V) |
| Context Window | Up to 128K | Up to 128K | Up to 128K | Up to 128K |
| API Compatibility | OpenAI ✅ | OpenAI ✅ | OpenAI ✅ | OpenAI ✅ |
DeepSeek: My New Default
I'll be straight with you — DeepSeek became my go-to by the end of week one. The price-to-quality ratio just felt unfair.
Their lineup has something for everyone. V4 Flash runs $0.25 per million output tokens, which honestly feels like a typo the first time you see it. V3.2 is $0.38, V4 Pro is $0.78, R1 (their reasoning model) is $2.50, and there's a Coder variant also at $0.25.
What blew my mind was the speed. V4 Flash was pushing around 60 tokens per second in my tests. Compared to my GPT-4o experience, it felt like the model was just blurting answers out before I even finished reading the prompt. For coding tasks specifically, I kept being surprised at how clean the output was. The functions just worked. Imports were right. Edge cases were handled. I'd heard DeepSeek was good at code but I didn't expect it to be this consistent.
The English output was solid too. Sometimes I genuinely couldn't tell whether the response came from DeepSeek or GPT-4o. There's also this whole "open-weight heritage" thing where they publish research and weights, which I think is cool for the AI community in general.
Where it falls short: vision. If you need to analyze images, DeepSeek isn't your friend. There's no native image understanding. Also, if you're doing heavy Chinese language work, GLM and Kimi tend to edge it out a bit. And the model variety is smaller than Qwen's — fewer size options to choose from.
Here's the basic call I ended up making hundreds of times during testing:
from openai import OpenAI
client = OpenAI(
api_key="ga_xxxxxxxxxxxx",
base_url="https://global-apis.com/v1"
)
response = client.chat.completions.create(
model="deepseek-v4-flash",
messages=[{"role": "user", "content": "Explain quantum computing in 100 words"}]
)
print(response.choices[0].message.content)
That snippet looks identical to what you'd write for OpenAI, which is exactly the point. Zero learning curve if you've used the OpenAI SDK before.
Qwen: The Model That Does Everything
Qwen is Alibaba's family, and what got me about it was the sheer variety. I counted six different models in their mainstream lineup, and prices ranged from $0.01 per million tokens all the way up to $3.20. There's literally a model for every budget tier.
The cheapest one, Qwen3-8B, is $0.01/M. I genuinely thought this was a mistake when I first saw it. One cent per million tokens. I ran it on some basic text formatting tasks and it worked fine for the simple stuff. Not as smart as the bigger ones, but for "summarize this paragraph" type work? Perfect.
Then you've got Qwen3-32B at $0.28, which I think is their sweet spot. Qwen3-Coder-30B at $0.35 for code stuff. Qwen3-VL-32B at $0.52 if you need vision. Qwen3-Omni-30B at $0.52 handles audio, video, and images in one model. And if you've got enterprise needs, Qwen3.5-397B at $2.34 is the heavy hitter.
The multimodal stuff is where Qwen really stands out. I hadn't really thought about needing video understanding until I tried Qwen3-Omni-30B, but it was actually useful for processing short clips for my side project. The Omni model handles audio, video, and image inputs in one API call. That's wild.
My one complaint? The naming is a mess. Qwen3, Qwen3.5, Qwen3.6, with various suffixes like VL, Omni, Coder — it took me an embarrassing amount of time to figure out which one to use for what. Bootcamp didn't exactly prepare me for enterprise naming conventions.
English performance is good but not DeepSeek-level in my tests. And some of the models feel overpriced relative to what you get. For example, Qwen3.6-35B runs around $1/M and the quality doesn't feel like it justifies it compared to the 32B at $0.28.
response = client.chat.completions.create(
model="Qwen/Qwen3-32B",
messages=[{"role": "user", "content": "Write a Python function to merge two sorted lists"}]
)
This is the one I kept using for general-purpose coding tasks. Reliable, fast enough, and the price is hard to argue with.
Kimi: The Brainy One
Kimi was the one I had the most expectations for, and honestly, it delivered on the reasoning side. Their main model, K2.5, runs $3.00 per million output tokens, and there's a bigger variant that hits $3.50. Yeah, that's pricey. You're paying premium here.
But the reasoning benchmarks are no joke. When I threw math problems and logic puzzles at Kimi, it consistently outperformed everyone else. There's something about how it works through problems step by step that feels different from the others. Like, it actually shows its work and the work is correct.
The downside? It's the slowest of the four. If you're building something that needs snappy responses, Kimi will frustrate you. It's also got no vision or multimodal capabilities that I could find. And at $3.00-$3.50/M, it's not a model you just casually throw prompts at.
For pure Chinese language work though? Kimi tied with GLM at the top. If you're doing translation, cultural context, or Chinese content generation, this thing is excellent.
I used Kimi less than the others just because of the price, but when I needed it to actually think, it delivered.
GLM: The Chinese Language Champion
GLM comes from Zhipu AI, and I'll be honest — this was the model I knew the least about going in. Now it's the one I recommend to anyone doing Chinese work.
Their model range goes from GLM-4-9B at $0.01/M (super cheap, basic stuff) all the way up to GLM-5 at $1.92/M for the heavy lifting. The middle ground has some good options too.
Where GLM really shines is Chinese. Like, it's not even close. If you're generating marketing copy in Chinese, translating idioms, or working with Chinese-language data, GLM feels native in a way the others don't quite match. Kimi is right there with it, but GLM often edges ahead on nuanced cultural stuff.
They also have GLM-4.6V for vision tasks, which I tested on some product image analysis. Worked well for OCR in Chinese especially. If you need to extract text from Chinese documents or images, it's a solid pick.
In my tests, GLM felt like the most well-rounded option for bilingual projects. English quality is good — not DeepSeek-level but reliable. The only real weakness I found was code generation, where it scored a bit lower than the other three. Not bad, just not as sharp.
What I'd Actually Tell You To Use
Okay, here's my honest take after all this testing. If I were starting a new project right now, here's how I'd pick:
For most day-to-day stuff — coding, content generation, general Q&A — DeepSeek V4 Flash at $0.25/M is the move. Fast, smart, cheap. Hard to argue with.
If I needed vision or multimodal capabilities, I'd go Qwen. The Qwen3-VL and Qwen3-Omni models are genuinely useful, and
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